Llama 3.1 NemoGuard 8B Topic Control vs Llama 3.3 Nemotron Super 49B v1
Llama 3.1 NemoGuard 8B Topic Control (2025) and Llama 3.3 Nemotron Super 49B v1 (2025) are compact production models from NVIDIA AI. Llama 3.1 NemoGuard 8B Topic Control ships a 4k-token context window, while Llama 3.3 Nemotron Super 49B v1 ships a 128k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 3.3 Nemotron Super 49B v1 fits 32x more tokens; pick it for long-context work and Llama 3.1 NemoGuard 8B Topic Control for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3.1 NemoGuard 8B Topic Control | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
| Best for | general production evaluation | general production evaluation |
| Decision fit | Classification | Long context |
| Context window | 4k | 128k |
| Cheapest output | - | - |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Local decision data tags Llama 3.1 NemoGuard 8B Topic Control for Classification.
- Llama 3.3 Nemotron Super 49B v1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Llama 3.3 Nemotron Super 49B v1 for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3.1 NemoGuard 8B Topic Control
Unavailable
No complete token price in local provider data
Llama 3.3 Nemotron Super 49B v1
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-01-01 | 2025-06-01 |
| Context window | 4k | 128k |
| Parameters | 8B | 49B |
| Architecture | decoder only | decoder only |
| License | 1 | 1 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 3.1 NemoGuard 8B Topic Control | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers |
Pricing not yet sourced for either model.
Capabilities
| Capability | Llama 3.1 NemoGuard 8B Topic Control | Llama 3.3 Nemotron Super 49B v1 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | No | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint is close: both models cover the core production surface. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
Pricing coverage is uneven: Llama 3.1 NemoGuard 8B Topic Control has no token price sourced yet and Llama 3.3 Nemotron Super 49B v1 has no token price sourced yet. Provider availability is 1 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose Llama 3.1 NemoGuard 8B Topic Control when provider fit are central to the workload. Choose Llama 3.3 Nemotron Super 49B v1 when long-context analysis and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, Llama 3.1 NemoGuard 8B Topic Control or Llama 3.3 Nemotron Super 49B v1?
Llama 3.3 Nemotron Super 49B v1 supports 128k tokens, while Llama 3.1 NemoGuard 8B Topic Control supports 4k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is Llama 3.1 NemoGuard 8B Topic Control or Llama 3.3 Nemotron Super 49B v1 open source?
Llama 3.1 NemoGuard 8B Topic Control is listed under 1. Llama 3.3 Nemotron Super 49B v1 is listed under 1. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Where can I run Llama 3.1 NemoGuard 8B Topic Control and Llama 3.3 Nemotron Super 49B v1?
Llama 3.1 NemoGuard 8B Topic Control is available on NVIDIA NIM. Llama 3.3 Nemotron Super 49B v1 is available on NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 3.1 NemoGuard 8B Topic Control over Llama 3.3 Nemotron Super 49B v1?
Llama 3.3 Nemotron Super 49B v1 fits 32x more tokens; pick it for long-context work and Llama 3.1 NemoGuard 8B Topic Control for tighter calls. If your workload also depends on provider fit, start with Llama 3.1 NemoGuard 8B Topic Control; if it depends on long-context analysis, run the same evaluation with Llama 3.3 Nemotron Super 49B v1.
Continue comparing
Last reviewed: 2026-05-14. Data sourced from public model cards and provider documentation.